Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis
文献类型：期刊论文
| 作者 | Zuo, Qiankun3,4; Lu, Libin5; Wang, Lin4,6; Zuo, Jiahui1; Ouyang, Tao2 |
| 刊名 | FRONTIERS IN NEUROSCIENCE
 |
| 出版日期 | 2022-11-28 |
| 卷号 | 16期号:-页码:- |
| 关键词 | functional brain connectivity temporal-spatial transformer alignment generative adversarial learning graph convolutional network early Alzheimer's disease |
| 英文摘要 | IntroductionThe brain functional network can describe the spontaneous activity of nerve cells and reveal the subtle abnormal changes associated with brain disease. It has been widely used for analyzing early Alzheimer's disease (AD) and exploring pathological mechanisms. However, the current methods of constructing functional connectivity networks from functional magnetic resonance imaging (fMRI) heavily depend on the software toolboxes, which may lead to errors in connection strength estimation and bad performance in disease analysis because of many subjective settings. MethodsTo solve this problem, in this paper, a novel Adversarial Temporal-Spatial Aligned Transformer (ATAT) model is proposed to automatically map 4D fMRI into functional connectivity network for early AD analysis. By incorporating the volume and location of anatomical brain regions, the region-guided feature learning network can roughly focus on local features for each brain region. Also, the spatial-temporal aligned transformer network is developed to adaptively adjust boundary features of adjacent regions and capture global functional connectivity patterns of distant regions. Furthermore, a multi-channel temporal discriminator is devised to distinguish the joint distributions of the multi-region time series from the generator and the real sample. ResultsExperimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) proved the effectiveness and superior performance of the proposed model in early AD prediction and progression analysis. DiscussionTo verify the reliability of the proposed model, the detected important ROIs are compared with clinical studies and show partial consistency. Furthermore, the most significant altered connectivity reflects the main characteristics associated with AD. ConclusionGenerally, the proposed ATAT provides a new perspective in constructing functional connectivity networks and is able to evaluate the disease-related changing characteristics at different stages for neuroscience exploration and clinical disease analysis. |
| 学科主题 | Neurosciences & Neurology |
| 语种 | 英语 |
| 出版者 | FRONTIERS MEDIA SA |
| WOS记录号 | WOS:000897641000001 |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35536]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.StateKey LaboratoryofPetroleumResourceandProspecting,andUnconventionalPetroleumResearch Institute,ChinaUniversityofPetroleum,Beijing,China 2.StateKeyLaboratoryofGeomechanicsand GeotechnicalEngineering,InstituteofRockandSoilMechanics,ChineseAcademyofSciences, Wuhan,China 3.School of Information Engineering, Hubei University of Economics, Wuhan, China 4.CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China 5.School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China, 6.Guangdong-Hong Kong-Macau Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China |
| 推荐引用方式 GB/T 7714 | Zuo, Qiankun,Lu, Libin,Wang, Lin,et al. Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis[J]. FRONTIERS IN NEUROSCIENCE,2022,16(-):-. |
| APA | Zuo, Qiankun,Lu, Libin,Wang, Lin,Zuo, Jiahui,&Ouyang, Tao.(2022).Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis.FRONTIERS IN NEUROSCIENCE,16(-),-. |
| MLA | Zuo, Qiankun,et al."Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis".FRONTIERS IN NEUROSCIENCE 16.-(2022):-. |
入库方式： OAI收割
来源：武汉岩土力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。